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THz Near-Field Imaging sensor Using Integrated Substrateless Silicon Interferometer

THz Near-Field Imaging Sensor Using Integrated Substrateless...

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German Microwave conference

作者： Shuya Iwamatsu Daniel Headland Israa Mohammad Masayuki Fujita Andreas Stöhr Department of Optoelectronics University of Duisburg-Essen Duisburg Germany Optoelectronics and Laser Technology Group Universiy Carlos III of Madrid Madrid Spain Graduate School of Engineering Science Osaka University Toyonaka Japan

Terahertz (THz) time-domain spectroscopy (TDS) technology has greatly advanced various THz applications ranging from fundamental science to industrial non-destructive imaging. However, traditional THz- TDS systems rely on complex setups with femtosecond pulse lasers and bulky free-space optical components, hindering their miniaturization. A more practical approach introduces THz frequency domain imaging using semiconductor laser diodes, which replace femtosecond laser and facilitate integration into photonic integrated circuit (PIC) chips. Despite its promise, addressing real-world imaging applications remains challenging due to the need for free-space optics, which also negatively impact resolution due to the diffraction limit. To overcome this limitation, this study explores the use of silicon (Si) microphotonics, focusing on an integrated Y -junction that operates as an interferometer. The Y -junction essentially replaces all free-space components, paving the way towards compact, monolithically integrated all-silicon structures that span just ~ 1 cm, We demonstrate near-field imaging at 325 GHz employing the Y-junction. The experiments achieve a 500-µm spatial resolution and a 100-µm depth resolution, showcasing potential for handheld THz near-field imaging sensors.

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Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization

Multimodal-to-Text Prompt Engineering in Large Language Mode...

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IEEE conference on Wireless Communications and Networking

作者： Harshith Manjunath Lucas Heublein Tobias Feigl Felix Ott Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability and efficiency, LLMs have not been extensively explored for signal processing tasks, particularly in the domain of global navigation satellite system (GNSS) interference monitoring. GNSS interference monitoring is essential to ensure the reliability of vehicle localization on roads, a critical requirement for numerous applications. However, GNSS-based positioning is vulnerable to interference from jamming devices, which can compromise its accuracy. The primary objective is to identify, classify, and mitigate these interferences. Interpreting GNSS snapshots and the associated interferences presents significant challenges due to the inherent complexity, including multipath effects, diverse interference types, varying sensor characteristics, and satellite constellations. In this paper, we extract features from a large GNSS dataset and employ LLaVA to retrieve relevant information from an extensive knowledge base. We employ prompt engineering to interpret the interferences and environmental factors, and utilize t-SNE to analyze the feature embeddings. Our findings demonstrate that the proposed method is capable of visual and logical reasoning within the GNSS context. Furthermore, our pipeline outperforms state-of-the-art machine learning models in interference classification tasks. Github: https://***/darcy_gnss

关键词： Global navigation satellite system Accuracy Large language models Knowledge based systems Pipelines Interference Feature extraction Prompt engineering Monitoring Context modeling

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Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cultivation

Smart Farming: IoT-Driven Crop Yield Prediction for Rice Cul...

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Soft Computing for Security applications (ICSCSA), International conference on

作者： Nandana Sumesh Navaneeth R Vimal Raj Vismaya Rajesh Ani R Department of Computer Science Amrita School Of Computing Amritapuri India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

In recent years, the agricultural sector has faced challenges in achieving optimal crop yields due to complex environmental factors and limited access to data-driven decision-making tools. An innovative IoT-based approach to predict crop yields is presented, aiming to improve crop productivity for farmers. The system utilizes a network of IoT sensors, including DHT22 and rainfall sensors, to gather real-time data on temperature, humidity, and rainfall. Soil-integrated sensors are also used to monitor specific soil properties such as nitrogen, phosphorus, potassium, pH, electrical conductivity, humidity, and temperature. This data is sent to a web interface for live display and stored for predictive modeling. Several regression models—decision tree, random forest, ridge regression, and linear regression—were implemented to predict crop yields, with the random forest regression model achieving the highest accuracy and an error rate of 6.46 percent. The stacking model was also analyzed, but the random forest model’s MAE and R- metrics proved superior, leading to its selection for deployment. A user-friendly web-based interface has been developed to enable farmers to interact with the system and predict crop yields effectively. This implementation smoothly integrates IoT technology with advanced data analytics, providing valuable insights to optimize agricultural practices, which in turn enhances food security and improves livelihoods.

关键词： Temperature sensors Rain Stacking Predictive models sensor systems Real-time systems sensors Crop yield Regression tree analysis Random forests

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD International Multi-conference on systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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IoT and BSN applications for Real Time Monitoring and Disease Prediction

IoT and BSN Applications for Real Time Monitoring and Diseas...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Jaishree Meena Vivek Shukla Amit Jain Vishan Kumar Gupta Paras Jain Amity School of Biological Science Amity University Punjab Mohali India Amity Law School Amity University Punjab Mohali India Department of CSE OP Jindal University Raigarh India Amity School of Engineering and Technology Amity University Punjab Mohali India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

IoT applications have been applied to health areas, so in the most advanced healthcare application environment, using them makes medical professionals and patients’ lives easier. BSN (Body sensor Network) technology is essential to the Internet of Things (IoT) in the healthcare system because it allows for the use of low-power, lightweight wireless sensor nodes to monitor patients. Healthcare systems utilizing BSN and IoT technologies are the subject of this paper. There are several sensors in this system, including blood pressure, temperature, and pulse rate sensors. The controller receives the data from these sensors after they have detected the parameters. When the temperature rises above the specified range, the buzzer will sound, per the conditions. For display on the LCD, it transports the sensed data. Doctors receive data via the internet at the same time, enabling them to provide prompt, appropriate solutions in real time. Many patients endure suffering because of not receiving prompt, appropriate assistance and solutions to their problems. Thus, the suggested system aids in an emergency and a real-time solution. Because this system is portable, it can be carried around by an individual. As a result, ongoing health monitoring is feasible. Moreover, the system uses a variety of supervised learning algorithms to forecast the illness for a specific patient based on their current reading.

关键词： Temperature sensors Machine learning algorithms Accuracy Medical services Prediction algorithms Real-time systems Internet of Things

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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International conference on

作者： Kancharla Nagababu S. Ananthi Neeraj Sunheriya Venkata Siva Prasad Ch J. Jayanthi Hamed Fawareh Department of Computer Applications Aditya University Department of CSE St. Joseph's College of Engineering Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of ECE Jeppiaar Engineering College Chennai-119 Department of CSE Sona College of Technology Salem Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies.

关键词： Couplings Accuracy Social networking (online) Scalability sensor phenomena and characterization sensor fusion Ventilation User experience Protection Standards

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Edge Intelligence-Based E-Health Wireless sensor Network systems

Edge Intelligence-Based E-Health Wireless Sensor Network Sys...

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International conference on Computer and Information Science (ACIS)

作者： Bintao Hu Matilda Isaac Anwar P.P. Abdul Majeed Hengyan Liu School of Internet of Things Xi'an Jiaotong-Liverpool University Suzhou China School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China

In order to support delay-sensitive applications of healthcare internet of things (HIoT) network systems, it is necessary to allow e-health wearable devices to process their computational applications at an artificial intelligence (AI) based mobile edge computing (MEC) server. Existing works mainly focused on optimising the offloading decisions of all users while minimising their corresponding energy consumption and/or transmission/processing delays. However, the huge data volumes offloading and/or privacy-preserving data processing have been largely ignored. In this paper, we consider an edge intelligence-based e-health wireless sensor network system, where the queues at the MEC server follow M/M/1 queueing model. To minimise energy consumption among all e-health wearable devices, we propose to jointly optimise the FL model task offloading decisions of all e-health wearable devices while guaranteeing delay constraints (which include the transmission delay, queueing delay and processing delay) and global/local FL training accuracy. This is achieved by devising an FL-based edge intelligence queueing offloading decision optimisation algorithm. Simulation results demonstrate that our proposed algorithm achieves a much lower maximum service delay than the benchmarks.

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dToF LIDAR System Using Addressable Multi-Channel VCSEL Transmitter, 128x80 SPAD sensor, and ML-Based Object Detection for Adaptive Beam-Steering 44

dToF LIDAR System Using Addressable Multi-Channel VCSEL Tran...

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44th Annual IEEE Custom Integrated Circuits conference, CICC 2023

作者： Wu, Yifan Zhou, Sifan Sun, Miao Xia, Tao Qian, Jian Wang, Lei Shi, Shi Cui, Lebei Wang, Chill Li, Yuan Yu, Hengwei Lin, Zhihong Qiu, Lei Qin, Yajie Sun, Min Bai, Rui Chen, Xuefeng Chiang, Patrick Yin Zhuo, Shenglong Tongji University The College of Electronics and Information Engineering Shanghai China Fudan University Shanghai China Southeast University Shanghai China Tencent Research China PhotonIC Technologies Shanghai China

ISBN: (纸本)9798350399486

Light detection and ranging (LiDAR) technology is increasingly being deployed in vehicles for advanced driving assistance system (ADAS) and is a potential critical technology for autonomous driving in the future. Currently, hybrid solid-state LiDARs based on micro-electro- mechanical system (MEMS) or rotating mirrors are the most mature solutions [1]. Unfortunately, these hybrid LiDARs still exhibit high risk of damage due to their limited mechanical reliability in harsh environments across many years of operation. Furthermore, these solutions require highly accurate calibration due to the complex optical alignment, resulting in higher production cost. Compared with these mechanical solutions, solid-state flash LiDARs without any moving parts are easier to mass-produce, more robust, less costly, and exhibit smaller form-factor, allowing flash-LiDAR to be embedded in multiple places around the automobile. Nevertheless, the detection distance of a single-flash LiDAR is short due to the smaller optical power limited by the laser eye-safety requirement, VCSEL thermal heating, and long-term VCSEL reliability [2]. Furthermore, for some applications, the distance of only one or two nearest objects needs to be accurately measured, and therefore empty parts of the field-of-view (FOV) are of limited interest. Unfortunately, single-flash LiDARs capture the point cloud of the entire FOV within each frame, resulting in high power consumption. © 2023 IEEE.

关键词： Object detection

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Applying Computational Intelligence, Visual Optimization Tools, and Synchronized PAYLOAD’s Handshaking to Enhance Greenhouses ECC Cyber Security-based Agriculture IoT’s Properties

Applying Computational Intelligence, Visual Optimization Too...

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International conference on Optimization and applications (ICOA)

作者： Salma Ait Oussous Abderrahim Bajit Yasmine Achour Ilham Morino Driss Zejli Rachid Elbouayadi Advanced Systems Engineering Laboratory (ISA) National School of Applied Sciences Ibn Tofail University Kenitra Morocco

One of the necessities of life is Food and its global availability has made agriculture a vital sector in recent years. With the development of technology, maintaining a good greenhouse climate has become a necessity to make plants more productive. The introduction of IoT technology solves many problems and offers new opportunities for improving efficiency and sustainability. Communication protocols such as MQTT and I2C play an essential role in data collection and exchange between agricultural IoT devices. However, data security remains a major challenge to ensure the confidentiality and integrity of transmitted information. Furthermore, payload optimization and the application of advanced techniques such as real-time data synchronization, psychovisual coding, and data computational intelligence are essential in increasing IoT Greenhouse’s performance. Data analysis tools are critical in approximating missing or erroneous sensor values, allowing farmers to make informed decisions. The combination of all these factors provides new solutions for achieving sustainable, energy-efficient agricultural practices. This article focuses on Psychovisual Optimization tools, ECC for PAYLOAD’s security and data analytics for prediction, discuss the choice of fixed and mobile nodes, and explore at how different approaches influence energy efficiency, environmental preservation, and overall performance’s system.

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An SAD architecture Verilog based for pattern matching

An SAD architecture Verilog based for pattern matching

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International conference on Wireless and Telematics (ICWT)

作者： Muhammad Adli Rizqulloh Erik Haritman Resa Pramudita Roer Eka Pawinanto Nike Saritka Agus Ramelan Industrial Automation and Robotic Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung Indonesia Department of Electrical Engineering Universitas Sebelas Maret Surakarta Indonesia

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

With the increasing importance of data security, encryption methods like advanced encryption standard (AES) have become pivotal in safeguarding sensitive information. However, the vulnerability of AES keys to side channel attacks (SCA) poses a significant threat. This paper proposes a novel approach using sum-of-absolute-difference (SAD) architecture for pattern matching, implemented on field-Programmable gate arrays (FPGAs) using Verilog. The SAD architecture, integrated with absolute difference (AD) architecture, offers an efficient solution for detecting the onset of AES computational processes without relying on victim signaling, crucial for real-world scenarios. The architecture's functionality and performance were validated through simulations using Vivado software, demonstrating its efficacy in detecting data stream similarities, with potential applications in embedded systems.

关键词： Wireless communication Wireless sensor networks Computer architecture Telematics Software Encryption Hardware design languages

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